{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "618105c0-4074-4061-a7c8-df11d16d7824",
   "metadata": {},
   "source": [
    "# Basic Documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff96884-5781-4617-bf6a-fd6748eb2307",
   "metadata": {},
   "source": [
    "The sole purpose of this notebook is to gradually present the features of **jaxKAN**, starting from the B-splines and ending with networks of KAN Layers. To run some cells of this notebook, libraries not included in `requirements.txt` are necessary. These are:\n",
    "\n",
    "```\n",
    "torch==2.3.0\n",
    "matplotlib==3.8.4\n",
    "```\n",
    "\n",
    "Note that all tests were performed for CPU (hence why we're installing torch and jax for CPU). GPU tests may also be available in the future, although going from CPU to GPU in JAX is more than straightforward."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a9f9d8-0dc5-42ac-a189-e88ab4ed6992",
   "metadata": {},
   "source": [
    "## Basis Functions: Splines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed236ef9-7dc6-4cb3-b6de-29b40ed7804f",
   "metadata": {},
   "source": [
    "The basis functions used to expand the activation functions are B-spline basis functions and their generation is handled by `bases/splines.py`. A B-spline basis is constructed using only a knot vector for a given spline order, $k$. In the present implementation, the concept of the knot vector is extended to the \"grid\", which is simply a collection of knot vectors per B-spline activation. The reason why a single knot vector is not utilized for all splines is because the choice of knot vector is data-dependent, so each different feature of the data requires its own knot vector, thus resulting in a grid of different knot vectors. Before diving into the specifics of KANs, let's visualize the spline basis for different values of $k$ and a uniform knot vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19e115f8-3a43-445e-b938-a54a54933b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax.numpy as jnp\n",
    "\n",
    "# Number of intervals in the knot vector\n",
    "G = 3\n",
    "# Knot vector's ends\n",
    "T = [0, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96787df5-2715-451c-8fd5-f01d5ffc3c84",
   "metadata": {},
   "source": [
    "For a given value of $k$, the knot vector is *augmented* by (uniformly) adding $k$ points to its left and $k$ points to its right. This augmentation turns the knot vector from $G+1$-dimensional to $G+2k+1$-dimensional. The function defined below creates a grid based on an augmented knot vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0639ed28-d581-453f-a06a-d3096b410aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_grid(size, T, G, k):\n",
    "    # Knot vector augmentation process\n",
    "    h = (T[-1] - T[0]) / G\n",
    "    grid = (jnp.arange(-k, G + k + 1, dtype=jnp.float32) * h + T[0])\n",
    "    # Expand from (G+2k+1) to (size, G+2k+1)\n",
    "    grid = jnp.expand_dims(grid, axis=0)\n",
    "    grid = jnp.tile(grid, (size, 1))\n",
    "    return grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d75083-5d42-4e65-b0d0-2fe97ece62ff",
   "metadata": {},
   "source": [
    "The `size` parameter is supposed to be $n_{in} \\cdot n_{out}$ for a KAN Layer, corresponding to the number of the layer's different spline activations, but for our tests we can simply set it to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f84756c3-2115-4095-a149-ba9690ad6d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from jaxkan.bases.splines import get_spline_basis\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "k_values = [0, 1, 2, 3]\n",
    "num_x = 200\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
    "\n",
    "for idx, k in enumerate(k_values):\n",
    "    grid = get_grid(1, T, G, k)\n",
    "\n",
    "    # Get xs\n",
    "    x = jnp.linspace(grid[0, 0], grid[0, -1], num_x)\n",
    "    x_reshaped = x.reshape((num_x, 1))\n",
    "\n",
    "    # Call the function\n",
    "    basis_splines = get_spline_basis(x_reshaped, grid, k)\n",
    "\n",
    "    # Reshape\n",
    "    funcs = basis_splines.reshape((x_reshaped.shape[0], basis_splines.shape[-2]))\n",
    "\n",
    "    # Determine the subplot position\n",
    "    ax = axes[idx // 2, idx % 2]\n",
    "    \n",
    "    for i in range(funcs.shape[1]):\n",
    "        ax.plot(x, funcs[:, i], label=f'Basis Function #{i + 1}')\n",
    "\n",
    "    # Adding labels and title\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('B(x)')\n",
    "    ax.set_title(f'Spline Basis Functions of Order {k}')\n",
    "    ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f257cb4d-3f73-4b73-ae22-7d14fa231b9c",
   "metadata": {},
   "source": [
    "The results shown above are expected. For a given grid of $G+2k+1$ points, the number of spline basis functions is equal to $G+k$, so for $k=0$ we get $G+0=3$ spline basis functions, for $k=1$ we get $G+1=4$ spline basis functions, and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce7bf9b9-7c7c-4e13-b3be-30fd8b0393f4",
   "metadata": {},
   "source": [
    "## Utilities: Least Squares Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41923329-401a-493d-8596-1ba41f1ec52a",
   "metadata": {},
   "source": [
    "The `utils.py` file holds two utility functions, designed to run the least squares algorithm for batched inputs, i.e. matrices of shape $(batch,M,N)$ and $(batch,M,K)$. While this functionality is already built-in for PyTorch, JAX's version of `jax.numpy.linalg.lstsq` does not accept batch-application of the Least Squares algorithm. In what follows, we show some examples and perform some tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a717a40f-7e77-4be7-af52-344df8fe88a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from jaxkan.utils.general import solve_full_lstsq\n",
    "\n",
    "# Set a manual seed for reproducibility\n",
    "torch.manual_seed(42)\n",
    "\n",
    "batch, M, N = 10, 100, 30\n",
    "\n",
    "A = torch.randn(batch, M, N)\n",
    "B = torch.randn(batch, M, 1)\n",
    "\n",
    "# Get the same matrices to compare results\n",
    "jA, jB = jnp.array(A.numpy()), jnp.array(B.numpy())\n",
    "\n",
    "torch_sol = torch.linalg.lstsq(A, B).solution\n",
    "jax_sol = solve_full_lstsq(jA, jB)\n",
    "\n",
    "# Compare results\n",
    "assert jnp.allclose(jax_sol, torch_sol.numpy(), atol=1e-5), \"The solutions do not match!\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d22cbee-20cc-461b-9799-c801bbf60215",
   "metadata": {},
   "source": [
    "No assertion error occurs, which implies that the two solutions are equivalent (up to 5th decimal precision). It would now perhaps be interesting to time the execution of the two approaches and compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eae177d5-f88a-416a-b40b-6a2996727718",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "# Function to time the torch implementation\n",
    "def time_torch_lstsq(A, B, reps):\n",
    "    start_time = time.time()\n",
    "    for _ in range(reps):\n",
    "        torch_sol = torch.linalg.lstsq(A, B).solution\n",
    "    torch_time = (time.time() - start_time)/reps\n",
    "    return torch_time\n",
    "\n",
    "# Function to time the jax implementation\n",
    "def time_jax_lstsq(jA, jB, reps):\n",
    "    # A warm-up run to ensure that the code has been compiled, because\n",
    "    # when jax.jitting the first run is always slow\n",
    "    jax_sol = solve_full_lstsq(jA, jB)\n",
    "\n",
    "    # Now start timing\n",
    "    start_time = time.time()\n",
    "    for _ in range(reps):\n",
    "        jax_sol = solve_full_lstsq(jA, jB)\n",
    "    jax_time = (time.time() - start_time)/reps\n",
    "\n",
    "    return jax_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4051980e-d9dd-490e-b070-a9be9a7bbe61",
   "metadata": {},
   "source": [
    "Let's first try a large batch size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79a6fc06-9043-402a-b1d0-fc4ba0ce5c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average execution time for the JAX version is 0.0371 seconds.\n",
      "The average execution time for the PyTorch version is 0.0505 seconds.\n"
     ]
    }
   ],
   "source": [
    "batch, M, N = 1000, 50, 50\n",
    "\n",
    "A, B = torch.randn(batch, M, N), torch.randn(batch, M, 1)\n",
    "jA, jB = jnp.array(A.numpy()), jnp.array(B.numpy())\n",
    "\n",
    "torch_time = time_torch_lstsq(A, B, 10)\n",
    "jax_time = time_jax_lstsq(jA, jB, 10)\n",
    "\n",
    "print(f\"The average execution time for the JAX version is {jax_time:.4f} seconds.\")\n",
    "print(f\"The average execution time for the PyTorch version is {torch_time:.4f} seconds.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1027895f-5876-4399-8f49-f97e0647874b",
   "metadata": {},
   "source": [
    "JAX appears to be faster. Let's now try to decrease the batch size and increase the matrix dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dc0314b1-8969-4518-b3cf-3e581c698416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average execution time for the JAX version is 0.2728 seconds.\n",
      "The average execution time for the PyTorch version is 0.0982 seconds.\n"
     ]
    }
   ],
   "source": [
    "batch, M, N = 100, 400, 400\n",
    "\n",
    "A, B = torch.randn(batch, M, N), torch.randn(batch, M, 1)\n",
    "jA, jB = jnp.array(A.numpy()), jnp.array(B.numpy())\n",
    "\n",
    "torch_time = time_torch_lstsq(A, B, 10)\n",
    "jax_time = time_jax_lstsq(jA, jB, 10)\n",
    "\n",
    "print(f\"The average execution time for the JAX version is {jax_time:.4f} seconds.\")\n",
    "print(f\"The average execution time for the PyTorch version is {torch_time:.4f} seconds.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63943442-3872-466e-9e00-6a0baf71d261",
   "metadata": {},
   "source": [
    "In this case, PyTorch achieves better results. So, as a rule of thumb, we could perhaps say that the JAX implementation is better optimized for large batches (perhaps due to the use of vmap), while the PyTorch implementation is better optimized for large matrices (perhaps due to the fact that the JAX implementation does costly matrix transpositions and multiplications under the hood). In any case, the results are comparable and therefore the JAX implementation is good, especially when considering that this process is not required for every training epoch."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a366e4e2-1d3e-4b83-8dea-7d57661145ff",
   "metadata": {},
   "source": [
    "## KANLayer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e594c1-3ee8-49cd-ac4c-dce3d0aae545",
   "metadata": {},
   "source": [
    "After these preliminaries, we may proceed to the concept of the KAN Layer. Each KAN Layer is defined by the number of its input nodes ($n_{in}$) and the number of its output nodes ($n_{out}$). Apart from its initialization and forward pass, it also includes a method which is relevant to the grid update which is supposed to occur periodically. After this update, the number of spline basis functions changes (because $G$ is now larger), therefore the basis coefficients need to be re-initialized. However, we do not re-initialize them from random values (otherwise all preceding training would be useless): instead, we perform the least squares algorithm to ensure that their initialization is such, that the corresponding spline activations are as close as possible to those before the grid was updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6abb54c1-36af-47e7-9922-bfd0d49ce5aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax\n",
    "\n",
    "from jaxkan.models.KANLayer import KANLayer\n",
    "\n",
    "n_in = 2\n",
    "n_out = 3\n",
    "\n",
    "# Get a random input\n",
    "key = jax.random.PRNGKey(0)\n",
    "x = jax.random.normal(key, (50, n_in))\n",
    "\n",
    "# Define the layer\n",
    "kan_layer = KANLayer(n_in=n_in, n_out=n_out)\n",
    "\n",
    "# Initialize the layer\n",
    "variables = kan_layer.init(key, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37bbb4f-180f-4b08-9c6a-d515dff0ba23",
   "metadata": {},
   "source": [
    "The `variables` object is a dictionary corresponding to the layer's variables. These variables are split in two categories: `state` variables, which correspond to non-trainable parameters and `param` variables, which correspond to trainable parameters. In this case, the grid corresponding to this layer is a `state` variable, while the spline basis coefficients (`c_basis`), the spline activation constants (`c_spl`) and the residual activation constants (`s_res`) are trainable `params`. The below defined function allows us to get a glimpse of the hierarchy of such dicts, as well as the shapes of the arrays they contain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ca67bde8-83da-4f70-b0c2-ae9e06c159e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_dict_hierarchy(d, indent=0):\n",
    "    for key, value in d.items():\n",
    "        if isinstance(value, dict):\n",
    "            print(' ' * indent + str(key))\n",
    "            print_dict_hierarchy(value, indent + 4)\n",
    "        else:\n",
    "            print(' ' * indent + f\"{key}: shape {value.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "841690e4-7f39-4fae-8acd-f6eaa1dd3ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state\n",
      "    grid: shape (6, 10)\n",
      "params\n",
      "    c_basis: shape (6, 6)\n",
      "    c_spl: shape (6,)\n",
      "    c_res: shape (6,)\n"
     ]
    }
   ],
   "source": [
    "print_dict_hierarchy(variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae5db72-b24f-4527-8936-d06b25f81fc7",
   "metadata": {},
   "source": [
    "By default, the first initialization of the grid is performed using $G = 3$ and $k = 3$ is selected if another $k$ argument is not passed. So, the shapes shown above make perfect sense: the grid is shaped `(n_in*n_out, G+2k+1)`, the spline basis coefficients are shaped `(n_in*n_out, G+k)` and the `c_spl` and `c_res` parameters are shaped `(n_in*n_out,)`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49093fad-14ee-44b3-a167-235a538a1c69",
   "metadata": {},
   "source": [
    "To perform a forward pass using this layer, we simply use the default `apply` method, passing the variables dict as well as the argument, `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "35d17dc4-d0a4-444c-9bc7-0f1276d34c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y, spl = kan_layer.apply(variables, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88db4048-7390-4ba8-bdbe-b123181cee41",
   "metadata": {},
   "source": [
    "The returned objects are `y`, the result of applying the layer on `x`, as well as `spl`, which is necessary for the regularization term of the loss function (see arXiv preprint, Eqs. 2.17 - 2.20)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "878daa2f-a204-412d-8eb5-4a8480d34d10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of y is (50, 3), i.e. (batch_size, n_out).\n",
      "The shape of spl is (3, 2), i.e. (n_out, n_in).\n"
     ]
    }
   ],
   "source": [
    "print(f\"The shape of y is {y.shape}, i.e. (batch_size, n_out).\")\n",
    "print(f\"The shape of spl is {spl.shape}, i.e. (n_out, n_in).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2a2b46-0471-48d4-8bd5-ce1d158656c1",
   "metadata": {},
   "source": [
    "Let's now perform a grid update for this layer, switching from $G=3$ to $G^\\prime = 5$. Again, the `apply` method is used, this time passing `mutable=['state']` as an argument, so that the grid is updated internally for the layer and returned as a new state variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ec0b7efb-c9ac-42d8-b945-24a4ccbff4bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the new coefficients array is (6, 8), i.e. (n_in*n_out, G'+k).\n"
     ]
    }
   ],
   "source": [
    "G_new = 5\n",
    "new_coeffs, new_state = kan_layer.apply(variables, x, G_new, method=kan_layer.update_grid, mutable=['state'])\n",
    "\n",
    "print(f\"The shape of the new coefficients array is {new_coeffs.shape}, i.e. (n_in*n_out, G'+k).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "629e43a0-0243-4da1-afed-df3cf33a9ec3",
   "metadata": {},
   "source": [
    "The `new_coeffs` object contains an array of the coefficients corresponding to the new grid, hence their shape. As for the `new_state` object, it's a dictionary which returns the new grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fbe4a935-e3b3-4a17-ba50-ec62f2d17ddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state\n",
      "    grid: shape (6, 12)\n"
     ]
    }
   ],
   "source": [
    "print_dict_hierarchy(new_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bacbeff-b2b5-4d3b-966f-f74a185b0bd7",
   "metadata": {},
   "source": [
    "As we can confirm, the new grid is shaped `(n_in*n_out, G'+2k+1)`, as it should. Of course, the grid is not an internal property of the layer; in fact, due to the way Flax works, none of the variables is. This can be confirmed if we perform a forward pass with the model after the grid's update."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3dfcffe1-5cff-4cc4-9bfa-98a15ed943fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_new, _ = kan_layer.apply(variables, x)\n",
    "\n",
    "# Compare y with y_new\n",
    "assert jnp.allclose(y_new, y, atol=1e-5), \"The solutions do not match!\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4407bf5b-990e-47a8-b35a-9ce5297dff82",
   "metadata": {},
   "source": [
    "Again, no assertion error occurs, which means that `y` and `y_new` are the same. So, how can we inform the layer that the grid has changed? We simply pass the correct variables directory when calling any `apply` method! Notice how for the above forward pass we passed the `variables` dict without applying the changes that came from `new_coeffs` and `new_state`. Let's combine them to create a `new_variables` dict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "894515ce-9241-47f6-8dde-ae23ee97c8bb",
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "The solutions do not match!",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[20], line 8\u001b[0m\n\u001b[0;32m      5\u001b[0m y_newer, _ \u001b[38;5;241m=\u001b[39m kan_layer\u001b[38;5;241m.\u001b[39mapply(new_variables, x)\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# Compare y with y_newer\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m jnp\u001b[38;5;241m.\u001b[39mallclose(y_newer, y, atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-5\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe solutions do not match!\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "\u001b[1;31mAssertionError\u001b[0m: The solutions do not match!"
     ]
    }
   ],
   "source": [
    "new_variables = variables.copy()\n",
    "new_variables['state'] = new_state['state']\n",
    "new_variables['params']['c_basis'] = new_coeffs\n",
    "\n",
    "y_newer, _ = kan_layer.apply(new_variables, x)\n",
    "\n",
    "# Compare y with y_newer\n",
    "assert jnp.allclose(y_newer, y, atol=1e-5), \"The solutions do not match!\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab036f92-d274-45b0-a67d-c207b8625a69",
   "metadata": {},
   "source": [
    "Indeed, we get the assertion error we hoped for! So the idea here is passing dictionaries back and forth when we need to apply internal parameter updates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb91034-fa7e-4fef-b8f4-820aeb7975e0",
   "metadata": {},
   "source": [
    "## KAN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed68576a-038c-42d5-9070-6cf3083b7e34",
   "metadata": {},
   "source": [
    "Based on the above, moving from a KANLayer to a KAN is pretty straightforward. A KAN with $F$ layers can be defined as a collection of KANLayers using an argument of the form `layer_dims = [n_0, n_1, ..., n_F]` where $n_i$ is the number of input nodes and $n_{i+1}$ is the number of output nodes for layer $i$. The full network's forward pass corresponds to consecutive passes through each of its layers (with the optional inclusion of a bias term), while a grid update for the KAN corresponds to a grid update for each of its layers. Of course, the dictionaries updates and passing is performed automatically here for the interior layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2852a57b-043c-431b-8661-140e40dd32a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jaxkan.models.KAN import KAN\n",
    "\n",
    "layer_dims = [4, 5, 2, 1]\n",
    "model = KAN(layer_dims=layer_dims, k=3, add_bias=True)\n",
    "\n",
    "x = jax.random.normal(key, (50, 4))\n",
    "\n",
    "variables = model.init(key, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "856cba2b-f681-41b1-b043-97230b5c5cbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "params\n",
      "    bias_0: shape (5,)\n",
      "    bias_1: shape (2,)\n",
      "    bias_2: shape (1,)\n",
      "    layers_0\n",
      "        c_basis: shape (20, 6)\n",
      "        c_spl: shape (20,)\n",
      "        c_res: shape (20,)\n",
      "    layers_1\n",
      "        c_basis: shape (10, 6)\n",
      "        c_spl: shape (10,)\n",
      "        c_res: shape (10,)\n",
      "    layers_2\n",
      "        c_basis: shape (2, 6)\n",
      "        c_spl: shape (2,)\n",
      "        c_res: shape (2,)\n",
      "state\n",
      "    layers_0\n",
      "        grid: shape (20, 10)\n",
      "    layers_1\n",
      "        grid: shape (10, 10)\n",
      "    layers_2\n",
      "        grid: shape (2, 10)\n"
     ]
    }
   ],
   "source": [
    "print_dict_hierarchy(variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351cd607-4c4f-46b9-9f6e-a5947fa6f5ad",
   "metadata": {},
   "source": [
    "The model's initialization handles each constituent layer's initialiation and all state and param variables are logged in a universal `variables` dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "59551939-5b51-4fb4-a30c-944c991792e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The input's shape is (50, 4), consistent with the first layer's input dimension.\n",
      "The output's shape is (50, 1), consistent with the last layer's output dimension.\n",
      "The length of the spl_reg list is 3, consistent with len(layer_dims)-1.\n",
      "Its constituent arrays are shaped:\n",
      "\tLayer No. 1: Shape (5, 4).\n",
      "\tLayer No. 2: Shape (2, 5).\n",
      "\tLayer No. 3: Shape (1, 2).\n"
     ]
    }
   ],
   "source": [
    "y, spl_regs = model.apply(variables, x)\n",
    "\n",
    "print(f\"The input's shape is {x.shape}, consistent with the first layer's input dimension.\")\n",
    "print(f\"The output's shape is {y.shape}, consistent with the last layer's output dimension.\")\n",
    "print(f\"The length of the spl_reg list is {len(spl_regs)}, consistent with len(layer_dims)-1.\\nIts constituent arrays are shaped:\")\n",
    "for idx, item in enumerate(spl_regs):\n",
    "    print(f\"\\tLayer No. {idx+1}: Shape {item.shape}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cadb6763-ab3e-4dd7-8e4f-47aefba233be",
   "metadata": {},
   "source": [
    "As we can see, the `spl_regs` outputs' dimensions are also consistent with the network's layers' dimensions. So let's check the grid update process in terms of the entire network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1210b9d-fcd3-4e3d-9552-2614181bb44a",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_new = 5\n",
    "updated_variables = model.apply(variables, x, G_new, method=model.update_grids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3803bbe-bd1d-4cfa-8bc7-e94a4cb7dd1b",
   "metadata": {},
   "source": [
    "This method returns the updated variables, to be used directly for the next model's application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f4b5dd18-cfb8-4cf7-9794-bdacbdf43ac0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "params\n",
      "    bias_0: shape (5,)\n",
      "    bias_1: shape (2,)\n",
      "    bias_2: shape (1,)\n",
      "    layers_0\n",
      "        c_basis: shape (20, 8)\n",
      "        c_spl: shape (20,)\n",
      "        c_res: shape (20,)\n",
      "    layers_1\n",
      "        c_basis: shape (10, 8)\n",
      "        c_spl: shape (10,)\n",
      "        c_res: shape (10,)\n",
      "    layers_2\n",
      "        c_basis: shape (2, 8)\n",
      "        c_spl: shape (2,)\n",
      "        c_res: shape (2,)\n",
      "state\n",
      "    layers_0\n",
      "        grid: shape (20, 12)\n",
      "    layers_1\n",
      "        grid: shape (10, 12)\n",
      "    layers_2\n",
      "        grid: shape (2, 12)\n"
     ]
    }
   ],
   "source": [
    "print_dict_hierarchy(updated_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2407b3f-7460-41a0-8739-a10f8bde52ef",
   "metadata": {},
   "source": [
    "All updated shapes are consistent with the $G=3 \\to G^\\prime = 5$ update. Note that, thanks to the way we're performing the updates inside KAN (using `unfreeze` when calling from `self.scope`), the updates do not occur in-place, i.e. the `variables` object is returned unchanged:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "728b6e1e-cb58-4859-88c2-c7441fc4e065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "params\n",
      "    bias_0: shape (5,)\n",
      "    bias_1: shape (2,)\n",
      "    bias_2: shape (1,)\n",
      "    layers_0\n",
      "        c_basis: shape (20, 6)\n",
      "        c_spl: shape (20,)\n",
      "        c_res: shape (20,)\n",
      "    layers_1\n",
      "        c_basis: shape (10, 6)\n",
      "        c_spl: shape (10,)\n",
      "        c_res: shape (10,)\n",
      "    layers_2\n",
      "        c_basis: shape (2, 6)\n",
      "        c_spl: shape (2,)\n",
      "        c_res: shape (2,)\n",
      "state\n",
      "    layers_0\n",
      "        grid: shape (20, 10)\n",
      "    layers_1\n",
      "        grid: shape (10, 10)\n",
      "    layers_2\n",
      "        grid: shape (2, 10)\n"
     ]
    }
   ],
   "source": [
    "print_dict_hierarchy(variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d4a3f5-a4e5-4a15-8e72-e5f4890e5bc7",
   "metadata": {},
   "source": [
    "This simply means that we have to pass the `updated_variables` dict in the next model application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd05443b-8281-47a2-b638-b466313bc44d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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